diffusion-kernel

You can optimize diffusion model kernels by following development guides to write custom JIT CUDA and Triton kernels, benchmarking them against PyTorch, and profiling with Nsight Compute to saturate GPU bandwidth.

The best way to profile diffusion model GPU kernels is using Nsight Compute and Triton benchmarks to analyze performance, achieve optimal memory bandwidth, and improve compute utilization across architectures.

Yes, you can use Triton kernels for fused elementwise operations in diffusion models, leveraging JIT compilation and provided reference templates to implement and test them within your framework.

Yes, the kernel optimization workflow supports GPU architectures including H100, A100, and T4, providing specific best practices for memory access, occupancy, and precision tuning for each.

You can speed up RMSNorm operations by following the add-cuda-kernel guide to implement a highly optimized JIT CUDA kernel, test correctness, benchmark against PyTorch, and profile with Nsight Compute.